The HOSE starts at the carbon atom whose shift is to be predicted, looks one bond away from the carbon and tries to find this
environment in its database. If it is successful it moves two bonds away and tries again and so on until it either comes across something not represented in the database or it reaches the boundary of the molecule.

The HOSE code approach works very well for query structures which are well represented in the reference collection. If atoms can be predicted to three spheres or more (NMRPredict goes to maximum of five spheres) the prediction can be considered to be very reliable.

However, if the query structure is not well represented in the database and the atom can only be predicted to one or two spheres the prediction cannot be relied upon at all.
Also the HOSE code approach exactly reproduces the contents of the reference database – including every error within the reference database.

If atoms can only be predicted to one or two spheres the only solution traditionally was to add your own similar data and use that for prediction. This is now possible with NMRPredict by using NMRPredict DBA. However, NMRPredict also uses a unique Neural Network algorithm, which is much more general and error tolerant than the HOSE code approach
and is much more accurate at predicting atoms it has not seen in its database.